Apr 10
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Stop building demos and start deploying production-ready AI. In this video, we move beyond localhost and take our RAG (Retrieval Augmented Generation) system to a real-world production environment. We demonstrate how to deploy our FastAPI-based application to Azure App Services. The core power of LLM Ops is on full display here: we are using the exact same codebase we built locally, adapting it for the cloud entirely through configuration. We even switch our LLM provider to Azure OpenAI without changing a single line of core application logic. What you will learn in this session: - The Deployment Workflow: Setting up Git, initializing your project, and preparing for a cloud push. - Azure Portal Setup: Creating a Web App, choosing the Python 3.12 runtime, and selecting the right infrastructure plan. - Scaling for Concurrency: Configuring Uvicorn with multiple worker nodes (-w 4) to handle simultaneous user requests. - The Git-to-Azure Pipeline: How to use Git Remote to push your code directly into the Azure container. - Production Latency Analysis: Comparing local Ollama performance against Azure OpenAI and understanding how infrastructure impacts user experience. By the end of this video, you will have a live, scalable API running in the cloud—the final step in turning an intelligent prototype into a reliable enterprise system.